code: def multiouput_module():
      """Return a simple classifier module class."""
  
      class MultiOutput(nn.Module):
          """Simple classification module."""
  
          def __init__(self, input_units=20):
              super(MultiOutput, self).__init__()
              self.output = nn.Linear(input_units, 2)
  
          # pylint: disable=arguments-differ
          def forward(self, X):
              X = F.softmax(self.output(X), dim=-1)
              return X, X[:, 0], X[::2]
  
      return MultiOutput
  
file_path: skorch/skorch/tests/conftest.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.datasets import make_regression
  from sklearn.preprocessing import StandardScaler
  import torch
  from torch import nn
label_desc: Use image node_id 4 for calling a global function with example usage: multiouput_module() and returns: MultiOutput, X,  X,  X